ChatGPT accumulated over a million users in just five days, an achievement that took social media ... [+]
Readily available data is the lifeblood of organizations in every industry. Over the past decade, the amount of data available has grown exponentially. This data—structured and unstructured—can be used to gain insights into customer behavior, identify market trends, and improve operational efficiencies. As a result, leaders are starting to rethink how they store, manage and connect every dataset for analytics and automation to increase productivity, make faster decisions and gain new insights.
Accepted as standard practice in years past, tiering data across multiple storage layers is no longer a practical option, reported Amazon Web Services last year. Instead, leaders increasingly need to be able to move data seamlessly between applications and workloads and do it all at scale. Therefore, they must understand their organization’s data infrastructure to ensure it can meet present and future operational needs.
Data has always been important for firms, but the age of Artificial Intelligence (AI) has taken it to a new level, concluded The Atlantic. In the past, data was used primarily for operational purposes such as keeping track of inventory or customer orders. Today, data is used to understand customers, personalize their experiences, and drive business decisions. Consider, for example, that this week, ChatGPT gained 1 million users in under a week. Fortune reported that the “AI chatbot is primed to disrupt search”. AI is not just about being able to crunch numbers, but rather it is about leveraging data to learn from the past and predict the future. That is why, according to a recent report by Grand View Research, the global AI market was valued at $93.5 billion in 2021 and is projected to expand at a compound annual growth rate of 38.1% from 2022 to 2030.
To succeed in this new economy, companies must invest in solutions that allow them to store and manage their data more effectively so they can make the most out of AI capabilities. Doing so will help them prepare for the future of work and stay ahead of their competition.
VAST Data is one firm experiencing explosive growth driven by the demand for infrastructure solutions that drive enterprise AI and data analytics. The company recently posted an annual recurring revenue four times higher than last year, with a 90% gross margin. Driven by the acquisition of new data-focused customers and the expansion of its largely petabyte-scale deployments globally, VAST Data expects its business momentum will continue in the coming months as the firm doubles down on the all-flash cloud, revolutionizes the data lake, and builds data platforms for the next wave of AI and machine intelligence. “As data becomes the most critical asset within the enterprise, organizations are waking up to the fact that they need more utility than legacy data platforms can provide to drive new AI solutions,” said Renen Hallak, founder and CEO, in an interview.
He believes leaders will demand fast and seamless access to their data to drive AI innovation. He argued that “the whole concept of tiering data is obsolete.” Instead, as data ages, it often becomes more valuable. VAST Data provides one system that collapses the pyramid of tiers—hot, warm, and cold data—allowing organizations to access all of it, anywhere, in real time. “We are confident that the market is ready for an all-flash data platform that can power any workload at any scale,” Hallak added.
Jack Tillotson, a lecturer at Finland’s University of Vassa, agrees. “A new platform is needed to unify data across all types of workloads and break down the silos legacy systems have created,” he said in an interview. He believes that data infrastructure is becoming more complex as organizations try to keep up with the ever-increasing volume, velocity, and variety of data—particularly true for companies dealing with big data sets that are growing exponentially.
To deal with this complexity, Tillotson suggested that leaders think about data infrastructure in terms of the “three Cs”: capacity, cost, and complexity. “The first two are relatively easy to understand,” he said. “Capacity is the amount of data that can be stored and processed, and the cost is the price of doing so. Complexity is a bit more nuanced, but it’s essentially the amount of effort required to manage the data infrastructure.”
Although data infrastructure complexity can’t be completely eliminated, Tillotson believes that it can be managed in a way that doesn’t impede progress. “The key is to simplify and automate as much as possible,” he advised. “By doing so, you can free up time and resources that can be better spent on more strategic initiatives.” Here are four tips to get you started:
1. The importance of data quality
Data quality is essential for making informed decisions and ensuring that your systems run smoothly. Conversely, poor data quality can lead to errors, inefficiencies, and lost opportunities. To provide high-quality data, leaders should put robust processes and controls in place to manage data collection, storage, and analysis. For example, they should establish clear guidelines for how data should be collected and ensure that data is stored in a secure and accessible manner. Furthermore, they should define “high-quality” data and implement mechanisms to monitor and improve data quality over time.
2. The need for scalability
As data volumes continue to grow, it’s essential to have a data infrastructure that can scale to meet the demands of your organization. This means having the ability to add more storage capacity and processing power as needed. Leaders should work with their IT teams to ensure that their data infrastructure is scalable and can meet future needs. For example, they can consider combining on-premises and cloud-based solutions to get the best of both worlds.
3. The value of data security
With the increasing importance of data comes an increased need for security. Leaders should ensure that their data infrastructure includes robust security measures to protect against unauthorized access and data breaches. For example, they can encrypt data at rest and in transit, implement access control measures, and use firewalls to protect their systems. Furthermore, they should plan for data recovery in the event of a disaster.
4. The essentialness of team collaboration
Data infrastructure is not a one-person job. A team of skilled professionals must design, build, and maintain a robust data infrastructure. Leaders should create an environment that fosters collaboration and ensures that everyone on the team has the tools and resources they need to be successful. For example, they can provide training on new technologies and processes, allow team members to work on challenging projects and encourage knowledge sharing.
The applications and algorithms that enable these new forms of business intelligence—unlike previously possible—require running workloads on data infrastructures that permit sharing and access in a random fashion, Hallak identified in a video interview. Legacy vendors tend to be very good at iterating on what they have but not very good at embracing a new paradigm, he said. “We’ve seen them try to compete by advancing stale infrastructure architectures, but AI and machine learning are initiatives that require clean-slate thinking.” Hallak predicts that next-generation infrastructure provides the high-speed, low-latency performance required by these demanding applications and algorithms, allowing them to operate at their best. Therefore, all data needs to be connected to become more innovative and valuable. Without it, the benefits of AI and machine learning will be hindered, limiting their usefulness and impact.
Leaders need to think carefully about their data infrastructure if they want to leverage AI and machine learning technologies. They need to ensure that their infrastructure is secure, scalable, and collaborative, and they must put robust processes in place to manage data collection and analysis. By doing so, they will be able to unlock the potential of these powerful technologies and reap the rewards of a modernized data-driven organization.
